CT Medical Images Denoising Based on New Wavelet Thresholding Compared with Curvelet and Contourlet

Authors: Amir Moslemi, Amir Movafeghi, Shahab Moradi

Abstract:

One of the most important challenging factors in medical images is nominated as noise. Image denoising refers to the improvement of a digital medical image that has been infected by Additive White Gaussian Noise (AWGN). The digital medical image or video can be affected by different types of noises. They are impulse noise, Poisson noise and AWGN. Computed tomography (CT) images are subjects to low quality due to the noise. Quality of CT images is dependent on absorbed dose to patients directly in such a way that increase in absorbed radiation, consequently absorbed dose to patients (ADP), enhances the CT images quality. In this manner, noise reduction techniques on purpose of images quality enhancement exposing no excess radiation to patients is one the challenging problems for CT images processing. In this work, noise reduction in CT images was performed using two different directional 2 dimensional (2D) transformations; i.e., Curvelet and Contourlet and Discrete Wavelet Transform (DWT) thresholding methods of BayesShrink and AdaptShrink, compared to each other and we proposed a new threshold in wavelet domain for not only noise reduction but also edge retaining, consequently the proposed method retains the modified coefficients significantly that result good visual quality. Data evaluations were accomplished by using two criterions; namely, peak signal to noise ratio (PSNR) and Structure similarity (Ssim).

Keywords: Computed Tomography (CT), noise reduction, curve-let, contour-let, Signal to Noise Peak-Peak Ratio (PSNR), Structure Similarity (Ssim), Absorbed Dose to Patient (ADP).

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1108951

References:

[1] Willi A. Kalender, “Dose in x-ray computed tomography’’, Physics in Medicine and Biology, Phys. Med. Biol. 59 (2014) R129–R150.
[2] Gonzalez. R. C and. Wood R.E, “Digital Image Processing”, 2nd edition, New Jersey Prentice Hall, 2002.
[3] R. R. Coifman and A. Sowa, “Combining the calculus of variations and wavelets for image enhancement,” Appl. Comput. Harmon. Anal., vol. 9, no. 1, pp. 1–18, Jul. 2000.
[4] Jean-Luc Starck, Emmanuel J. Candès, and David L. Donoho, “The Curvelet Transform for Image Denoising’’. IEEE Transactions on Image Processing, Vol. 11, No. 6, June 2002.
[5] Minh N Do and Martin Vetterli, Fellow, IEEE, “The Contourlet Transform: An Efficient Directional Multiresolution Image Representation’’, IEEE Transaction On Image Processing, December 2005.
[6] Shao-Weidal, Yan-Kuisun, Xiao-Lin Tian, Ze-Sheng Tang, “Image Desnoising Based On Complex Contourlet Transform’’, International Conference on Wavelet Analysis and Pattern Recognition, Beijing, China, 2-4 Nov .2007.
[7] J. Hou , J. Tian, and J. Liu, “An improved Wienerchop algorithm for image denoising’’, in Proc. of the IEEE International Conference on Communications, Circuits and Systems (ICCCAS), vol. 2, pp. 838–841, Oct. 2005.
[8] S. G. Chang, B. Yu, and M. Vetterli, “Spatially adaptive wavelet thresholding with context modeling for image denoising,’’ IEEE Trans. Image Process., vol. 9, no. 9, pp. 1522–1531, 2000.
[9] S. G. Chang, B. Yu, and M. Vettereli, “Adaptive wavelet thresholding for image denoising and compression,” IEEE Trans. Image Processing, vol. 9, no. 9, pp. 1532–1546, 2000.
[10] R. R. Coifman and A. Sowa, “Combining the calculus of variations and wavelets for image enhancement,” Appl. Comput. Harmon. Anal., vol. 9, no. 1, pp. 1–18, Jul. 2000.
[11] Mantosh Biswas and Hari Om, “An Image Denoising Threshold Estimation Method” Advances in Computer Science and its Applications (ACSA) 377 Vol. 2, No. 3, 2013, ISSN 2166-2924.
[12] A. K. Velmurugan and Dr. R. Jagadeesh Kannan, “Wavelet Analysis For Medical Image Denoising Based on Thresholding Technique”, International conference on current Trends in Engineering and Technology, ICCTET’13, pp.213-215,2013.